1. Field of the Invention
The present invention relates to a forecasting apparatus for predicting future events by using previously accumulated historical data.
2. Description of the Background Art
Generally, there exist various kinds of events of which historical data can be accumulated. These events include traffic-related phenomena, such as changes in travel time, traffic volumes or traffic jam conditions in transportation by roadways, railways, elevators, and so on, changes in total power consumption in the electric power industry, as well as changes in stock prices in the economic sector, which are expected to occur with some periodicity or reproducibility related to human activities. It is often needed to calculate forecast data from the accumulated historical data so that appropriate measures can be taken to cope with future changes.
In the sector of road traffic networks, for example, the volume and accuracy of historical data obtainable in the future are expected to significantly increase due to anticipated additional provision of various sensors, such as vehicle sensors, and introduction of probe cars.
In predicting future events, a forecasting apparatus must meet a variety of needs with respect to the intended time span of prediction, that is, how far into the future the forecasting apparatus should predict future events. In traffic-related phenomena, for example, there are cases where long-term forecasts (covering a prediction time span of a few hours to a few days) are necessary, such as when changing departure time or transport means to be used before departing. Also, there are cases where short-term forecasts (covering a prediction time span of a few seconds to a few or few tens of minutes) are necessary, such as when one is in need of route guidance after departing.
Furthermore, it is known that road traffic networks have considerably varying characteristics depending on areas (urban or local areas) and road types (ordinary roads or highways). Hence, a forecasting method which achieves a high degree of prediction accuracy in one area does not necessarily give good forecasting results in other areas. In practical applications, an optimum length of data to be accessed (or search data) for use in traffic prediction differs with areas and road types, for example. Optimum choice of historical data including kinds thereof and data from how far upstream or downstream of a particular roadway, for which a traffic forecast is to be made, should be taken into consideration in the traffic prediction also varies significantly depending on areas and road types.
Furthermore, currently unavailable data may become obtainable in the future as a result of additional provision of sensors, or currently available data may become unexploitable due to removal of unnecessary sensors, failures of particular sensors or deterioration of communication conditions. Under such circumstances, there is the need for a forecasting method which can efficiently handle and analyze a great deal of data and flexibly cope with differences in data structure and missing data values.
Japanese Laid-open Patent Application Publication No. 2000-67362 proposes a prior art forecasting method based on a pattern matching technique for predicting future events in road traffic systems, such as travel times, traffic volumes and traffic jam conditions, which are expected to occur with periodicity or reproducibility related to human activities.
FIGS. 15A and 15B are diagrams illustrating the prior art forecasting method disclosed in Japanese Laid-open Patent Application Publication No. 2000-67362.
Referring to FIG. 15A, a search data 1005 includes M number of historical data values taken from N number of time series historical data values (M≦N) expressing past conditions, while a forecast data 1006 includes P number of latest data values positioned ahead of the search data 1005. A historical data pattern table 1004 shown in FIG. 15A includes latest L number of combinations of the data 1005 and the data 1006.
Referring to a flowchart of FIG. 15B, when new historical data have been obtained, a judgment is made to determine whether it is necessary to update the historical data pattern table 1004. If it is judged to be necessary to update the historical data pattern table 1004, the oldest combination of the search data 1005 and the forecast data 1006 is abandoned and a new combination of the data 1005 and 1006 is added to the historical data pattern table 1004 in step 1001.
If it is necessary to obtain forecast data for a particular event, a search key 1007 is generated by using M number of historical data values taken from N number of time series data values (M≦N) expressing conditions at a point of prediction. Then, in step 1002, a search data 1005 having a pattern most resembling a pattern of the search key 1007 generated as shown above is searched from the latest historical data pattern table 1004. When the search data 1005 having the pattern most resembling the pattern of the search key 1007 has been extracted, the forecast data 1006 including P number of data values corresponding to the extracted search data 1005 is output as a current forecast data 1010 in step 1003.
The aforementioned forecasting method based on the conventional pattern matching technique proposed in Japanese Laid-open Patent Application Publication No. 2000-67362 has pending problems which are discussed below.
When obtaining the forecast data 1010, it is necessary to search through the historical data pattern table 1004 using the historical data values expressing conditions at the point of prediction as the search key 1007. Therefore, processing time needed for performing search operation increases as the amount of historical data values or historical data patterns increases.
Generally, registering multiple sets of historical data having a similar pattern in the historical data pattern table 1004 does not make any sense. Thus, measures must taken to avoid registration of multiple historical data sets having a similar pattern in the historical data pattern table 1004. For this purpose, it would be necessary to examine whether a historical data pattern similar to newly obtained historical data is already registered in the historical data pattern table 1004 and to abort updating of the historical data pattern table 1004 if a similar historical data pattern already exists in the historical data pattern table 1004, for example. As it is necessary to perform such a procedure, processing time needed for updating the historical data pattern table 1004 also increases as the amount of historical data values or historical data patterns increases.
Furthermore, it would be necessary to prepare different historical data pattern tables 1004 to cope with differences in the prediction time span, that is, how far into the future prediction should be made, in the length of search data, and in data structure such as kinds of historical data used for prediction. Also, the kinds of usable historical data may dynamically increase or decrease due to additional provision of sensors or removal of existing sensors while a forecasting apparatus is being operated. Should such a situation occur, it would be necessary to rebuild the historical data pattern tables 1004 in response to data structure changes, and this imposes a good deal of work load.
Furthermore, since the forecasting method based on the conventional pattern matching technique accumulates historical data patterns themselves in the historical data pattern table 1004, the data capacity necessary for accumulating such historical data increases with an increase in the amount of historical data values or in historical data patterns. In addition, the prior art forecasting method can not fully cope with a loss of historical data caused by failures of sensors or system maintenance.
Moreover, the prior art forecasting method is intended to give a typical forecast value from a broad point of view, such as travel time needed for a vehicle to travel a fixed road section, for instance, without directly taking into consideration driving behavior or preference of each individual vehicle. It is therefore difficult for the prior art forecasting method to satisfy such requirements as to directly use a data structure in which historical data are data on moving history of individual moving objects.